Robust non-contact peripheral oxygenation saturation measurement using smartphone-enabled imaging photoplethysmography

We propose a robust non-contact method to accurately estimate peripheral oxygen saturation (SpO(2)) using a smartphone-based imaging photoplethysmography. The method utilizes the built-in color camera as a remote sensor and the built-in flashlight as illumination to estimate the SpO(2). Following the ratio of ratios between green and red channels, we introduce a multiple linear regression algorithm to improve the SpO(2) estimation. The algorithm considers the ratio of ratios and the reflectance images recorded at the RGB channels during a calibration process to obtain a set of weighting coefficients to weigh each contributor to the final determination of SpO(2). We demonstrate the proposed smartphone-based method of estimating the SpO(2) on five healthy volunteers whose arms are conditioned by a manual pressure cuff to manipulate the SpO(2) between 90 similar to 100% as detected simultaneously by a medical-grade pulse oximeter. Experimental results indicate that the overall estimated error between the smartphone and the reference pulse oximeter is 0.029 +/- 1.141%, leading to a 43% improvement over the conventional ratio of ratios method (0.008 +/- 2.008%). In addition, the data sampling time in the current method is 2 seconds, similar to the sampling cycle used in the commercial medical-grade pulse oximeters. (c) 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

Automated summary

A non-contact method for accurately estimating peripheral oxygen saturation using a smartphone is proposed in this study. By introducing a multiple linear regression algorithm, the proposed method shows an improvement in estimation accuracy compared to conventional methods. Experimental results demonstrate the potential clinical application of this smartphone-based method, with data sampling time similar to commercial medical-grade pulse oximeters.